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Analyzing and Visualizing a Model

The results of a hypatia model

When a model is solved, the results will be postprocessed and uploaded to the interface in the format of a nested Python dictionary. The results dictionary classifies the outputs of the model in the following hierarchy:

  • Variable Level
    • Region Level
      • Technology Type Level
        • Values as pandas DataFrames

For example, for a model that is solved, user can have access to variable production_by_tech for the Supply technologies in region1 as follow:

model.results['production_by_tech']['region1']['Supply']

Visualization

Note

Hypatia visualization module is under active development.

Hypatia supports a couple visualization routines.

To use the plot functions, you need to set up the configuration of the plots like the colors or names through a multi-sheet excel file. As before, you can create the raw excel file by the model in the proper structure and fill it accordingly:

model.create_config_file(
  path = 'path/to/directory/config.xlsx'
)

This function will create an excel file with the following structure:

image

In order to have meaningful plots, it is necessary fill the tech_group and fuel_group in the correct way. For example, you may consider all the power plants as a single tech_group or different type of electricity flows (to final user, intermediate, and ...) as a single fuel_group. Once the config file is ready, you can create an instance of the &Plotter object. Besides the config file, you need to specify if the model has an hourly resolution or not.

from hypatia import Plotter

plots = Plotter(
  results = model,
  config = 'path/to/directory/config.xlsx',
  hourly_resolution = True, # if model has an hourly resultion otherwise False
)